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Found 6 papers, 3 papers with code
ELITR-Bench: A Meeting Assistant Benchmark for Long-Context Language Models
Our experiments with recent long-context LLMs on ELITR-Bench highlight a gap between open-source and proprietary models, especially when questions are asked sequentially within a conversation.
SARDINE: A Simulator for Automated Recommendation in Dynamic and Interactive Environments
Simulators can provide valuable insights for researchers and practitioners who wish to improve recommender systems, because they allow one to easily tweak the experimental setup in which recommender systems operate, and as a result lower the cost of identifying general trends and uncovering novel findings about the candidate methods.
Generative Slate Recommendation with Reinforcement Learning
Our findings suggest that representation learning using generative models is a promising direction towards generalizable RL-based slate recommendation.
Offline Evaluation for Reinforcement Learning-based Recommendation: A Critical Issue and Some Alternatives
In this paper, we argue that the paradigm commonly adopted for offline evaluation of sequential recommender systems is unsuitable for evaluating reinforcement learning-based recommenders.
SmoothI: Smooth Rank Indicators for Differentiable IR Metrics
To address this shortcoming, we propose SmoothI, a smooth approximation of rank indicators that serves as a basic building block to devise differentiable approximations of IR metrics.
Deep $k$-Means: Jointly clustering with $k$-Means and learning representations
We study in this paper the problem of jointly clustering and learning representations.
